{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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AxpljMQcFTWJ3reyLZIt37z/KNeNqUG36HYSZulSUK9XE49WFqYNo6DGgs/taOTJ2JrYizjloMqlUx7Fuyz7lPnelMH/NVlx42+OYr1kx6M6fI8KqTbsx3cqhp2ABeLU64MZtB5Qaq+r+w+4pyiogqAxtmAbtfh9RhbmLyd+xVS0ChamFCPQY0Nl9Z07vMtbKWhHnrIvzdilXqnWCw2uaAFDrLlQqV7Bq0+5Jwl13/nFmMOxOSafOjMHK0aTzqEwdJvfp1fAHh0s4dbp+EvUf4wrJXEBETJDpxf99NErQ/UlfUyEuRKDHgO5lLWsaOqiIO855cLiEdVv2hWq9fs0xSFP2OgBdM4I3xlsVRlgd51ovUxfV6kB3n3miuvhxVwD6TVxEqPUo9QvJ8YCaRUEatOnKobfbgpXTTxpBf8dWZ6GK9j91EIEeAz3dVqTtKuLsnDM4XMLqR/YYxXL7JyPTFYFK4AQJTT/e1UGQtv0X71uMu1b2AQBWOU7GP354t1LIMts9S91oFhNB7NLMCokADH/2Smx872L0Kv7mYX/HIHNQs4JYtP+pRahTlIjOB3A/gNcDmABwLzN/0XcMAfgigHcDGAXwu8z8TPzDTSc6ORalkGWcnXM2bjtQpxXr8GuOURx/rnCIIji9bNx2oC6226W326qFX3qvETZJuVE8USYXwL5vVVijyffhfoeuozpqeKTuGgTUtjea19CsM1gqOmYLkyiXMQCfZOZniOg1AHYR0beY+UeeY94F4CLn5zIAX3H+nRK8pBEyuu26lySu6nmmWrZKc1x91QKsfmSP0YSgaoMWhVK5oq1Jw2wLrmUbnox8jSBhruuatHzhbGWU0fWXFOu6P3lRfYdR/46mbQQbyWtoZuWRxgqTQjChJhdmfsHVtpn5lwCeBeD/a14H4H622Qmgh4jOjX20KSWK/bsdS2ATu3tvt6WsaTKwpIiZ08PneStPkbVgP3kibdkW1yTTrDPSCwG4ZvG5ytou2/ePKDXZ7ftHJh3fbeVqxcPyRDWbfSO4tm1vRBDBnnR034sriE3t4s34ZqTCZPaIZEMnovkAlgB4yrerCOCI5/ejqBf6IKJbiGiIiIZGRkYiDjW9RLF/t+MlWX3VgkAHHQC8Up3Q7jNx5lbHGd1Wcy6YsAlh47YDCLmNSDCATU/bj+nyhbORI0KpXMEnH96jnTiOlSsYWFLEjjVX4K6VfWBQzZQ2zowHdx7WhnUGMThcwupH99Qmdjci6Kalc3F6TP+3cU1DpkpB0LMZNilIhcnsYZxYREQzAWwGcCszv+zfrfhI3dvKzPcCuBewOxZFGGeqiWL/btVLcvvgXjz01BGMMyNPhKVv6MW+Y7/U2py9y3e/CcjKE86EpN8DwGh1AjnYjpVWEKd27lIdZ9y6afekbYEmmm4LyzY8iWPlCnJUvypRRf6YaOzrv7mvLtS1Os548KnDWt+LK4ij2MV1zyaAUHNKsw3MhfZjJNCJyIItzB9k5scUhxwFcL7n9/MAHGt+eNnB1G7aipfk9sG9eGDn4drv48zY8dxxfGDpXNwxcDEuWLNVuYQvlSuYv2Zr3bYotEqYpwErTzj5ylitiUjYiiKKjVvVmAQIdqS7JrJVvgnJRacUqJ5NlW/CP/64Km8K7SN0zexEsHwNwLPM/AXNYVsAfJBslgJ4iZlfiHGcqcfUphlneKLLgx5h7uWBnYdDk2oENcWeAmZM6zKOFnJplTnCW0Ezqs9G9VzqJm7v9rhqyQvtw0RDXwbgZgB7iWi3s+0zAOYCADPfA+Bx2CGLP4Ydtvjh2EeaYqJEA8QZnuheO0jkhCXVCHpM4vj9mOYe9Ghq/+jw/glNNeeg5zKvMB+5271I39JsQZzQy97f389DQ0OJXDtu4uwsb4qqQXQYeSJMOGn5QmsgADctnYvt+0cCJ2w3+ct0BUAADm64etLnw5SCoOcyyLT2vOc6Qvogol3M3K/aJ9UWY8DU0RlXkoZf8zJlghkHN1wdWmiKALztwlnY8dzxyGOb6jAwyZ8R1KkJsFdqJn6LswuvOmfdZydMWQjKQNWtEIri8Mw0ItBjQOfo9L6EZxcsnDozVotsCDLLhAn+qGnt3nECdsjeAxq7u6thBjlTOwFyMnfacX9BESgDS4qh37OVI5w6M1YTwKYRNUFZrm7RNO8KQWe2CXoWJZM0XUgtlxhQOTqtHOHlV6q1WOFypVoXpqaKPzeJMW7E8eZ9Wbf+QO2vzhPhrpV96J83C8s2PNmxwhywbdLtvD/d32xwuBTotC72FDBzepfRs+MnqNpmdZxh5almM1clSYU9i1InJn2IQHfQRQOYRK+oogGsPMHEPOp/0U0Sj6KGOHprkd8+uFdrdx9nxtCh47GUixUmw0Dd8+MKRJVzsmDlcffKPuxYc4U20StsYnefSx2j1YnatceZa4XNXMKexbD9UuWx/YjJBfpogKFDxyfV8QiLXvFu88d36/AL5yC7p0tQT1EV3lrkuhBHF50pJg4IwF0r++oSe6YKbk35WzftRrGngFOnx5R/wzzRpPDAZnIX3IbdJhO03zQU5hsK2i91YJJBNHToNY2HnjrS0jR9lc3y7II+7M2r4ZzV1difLkkzCgNTVpi7eDNLdWGLE8w1oTc4XMKoohsWABw/ddpI+w1rdOLFK6TD4t2D9ksdmGQQDR3BvTejHO+lt9tSmjZyZNtvvSnY3uiFU6f1YYjuy9BMyVoh/fR0W+hb/4RBqWA7T9ek/ywwOfdh9MyY8vn0CumwePeg/VGzWYV4EA0dwd1yohzvZe2KRbDykz9v5QlfeF8fDm64uhZy5ncqBdTMwrFypeEIF6G95AhopHaZlSe8NFqNnNSk0369FR0B2+S1Y80VWLtiUWjGclimaND+uDtwCWaIhg69pqGqhR01tCvO8MM5PYVYNJx8jjAeMaXdpeiMoZMjYOJggmHkFPczY1pXQxmqgDrvwW/HXrVpN4YOHccdA7azdN2WfbXrTVfMQGGZorr9Ydp9VsIdszJOFxHoCE7H7583KzQON8j5E/THjxpJMnpmLFCQFnsKKI+ewakzwZNEo8IcAE6dHkOPxpwkNE+jwhyw7fMX3vY4xpm1TleGXfunf94sAJhUqvfEaDU2x2XQO5UVh2lWxulFUv+bpJG0/0bS9sPotnI4y8qLoBWMcDNCGy1ZYaq5qo7TRd20slRGIyRR0sMESf1vIVHrmzeath/GaHUCo0EGeKEpWln3PSoE23HK3LhGH2S6c5tTqzRrVxh7W+TpNFedhqt79tPmMM1igw8R6E2iMz/oqu6Z2M1V/SSTIKyIUxQIQPe0fKg5KK2kRZj7tcNGyzPMCdDQVc2p/TkZJv1OdaGLukqPaXOYZrHBh0S5NInOYuXf7kYbhAnI3m4Ld63smxQ50GtYkjUKYe3jip6wyjhgILPCPC1EzVsAgBnT8nXtxNzzqOLTdc2pVTkZfvyaa1A4cNw9AVpBK3oXtBrR0JvkJc2S17s9ipnF7XbvX7rGnZATZJ4hAPNfV9DGEgtqWm2WefPcs+uei1OapCPAFj6f/207miUs2srdp1M4TGrq+zVX3fmKHlt6mqNH4u5d0A5EoKO50CSTZVmU8ET/BOGOTQdRcNuyRnjbhbPwveeOp8LskyXOdlZSrXJMf++54xgcLtXs2Z98eI9W0HpXWLpn29+HdvnC2di+f0T5POvMJC4qzTUodDErjTOyMk6XKS/Qbx/ciwd3Hm642a9J95goThTvRGCi2bciSOl7PxFh3ggnRquYMc0sxd6PycTMsIXz0KHjk57ZunMB2LHmisCwu6FDx+v60D6w8zCsnJ3c5K3uqMvJcM0zRY0SlEUNN+tM6bDFweESVm3arXwxooQmhWn4ujRuv72yYOVrmXZhGphLmOYkdB4mTvPebruBherRCHtmrBxh5vQulEeryigXEc7JImGLGjZuO6B9MaJo1UHLMp2d08oRVl56vrJVWVBZVT/jzKmJihHag8nfOsjsE/ZcVScY3dO6MPzZKydtz5r5YSoypQV6kNCOKzRp/Tf31TUnAICZ07tq6dd+opYEEGEuxE2aY60FPVNaoOscmgQYhyYFLUMHh0taTenEaBXLNjypXLbKyyQkjavQiJklW0xpga5yaLo9NU0e2rBaD2G1n3UO2KDwMUHw04qkrdEzY7h9cK+2wQsgzs40MqUTi1TlP+9a2ac1hfgJK+Jvommryp5GaUggCHet7MPnf/viugSiZjgxWsUDOw8rn+91W/ZJL9GUMqU1dKA5R09YrQdTTbvktOzy1pkGYNw6TJjauM9Lu7pBqSK2VKn/QvuZ0hp6s4QV8Y+iafs1nIElRexYc0WtKp4gqOjxpP4n/axE9f1IE+n4EYHeBGG1HvwmnZ6CVdfFyEXXcSbNdSOE5ClXqpi/Ziv61j+B5QtnJzqWKJFhrv8pzGwjQj8aU97k0gwmmXD+Y4KaQ+jKlnq7ygiCinKlik3fP4IZCVW0jNqNKMj/5I0Sy1qDiaQRgd4kYTZ4f2mBE6NVbSKQqmwpAFyz+NzAVG9BAOyEoLE2CvM8ESaYG+pGZFJr3ETot5u0h3GKQG8hg8MlpSDWCWZV2dL139yHV6oTIswFI9r5nEww4+CGqydtMxXCJkXt0tZgIgsrBhHoPuKcgYNKC5giLeWEpJgxLY8zYxOoanrQzukp1L0vuqgsvxDW5YC4ZsfVVy1IXYOJNK4Y/IhA99DsDGz6cAtC2nl+w9WBDVkKVh7LF86ue1905kS/EPaH5qpa2qkqPCbZYCJtKwYVoVEuRPR1InqRiH6o2X85Eb1ERLudn8/GP8z2EJYoFITKa99soocbGROFQkgnIkEw4YI1WwMVkjvfczG27x+pe18Y0HZI8uMNzVWZG7fvH8H1lxSRJ/uMeSJcf0lyBcLCwpTTgMnbfx+Ad4Yc8x1m7nN+Ptf8sNS0OoSpmRlYNRmoHu4oMIB11y6KdI5KSKNob1ZsnJmFQmcRZCos9hQwsKSoFfhujXT3OXNLQuvQvV+lcgWbd5Vq1SHHmbF5Vymx0MUstKQLNbkw87eJaH4bxhJIOxwSzdjsdA+l+3A3YoZxe4kWrFxgy7goeK8vjlYhKqYCzLSXAKB/7/JEqbJZZ6FhR1w29LcS0R4AxwB8ipn3qQ4iolsA3AIAc+fOjXSBdjgkTLoP6Qjqn+h9uE0aRbucGK22LJ1b7PtCI/j7muq4YM1WY4Gne+90JaSTtFmnvSZ8HAbXZwDMY+bFAP4SwKDuQGa+l5n7mbl/9uxoWW3tcEioinWFLRddTJdjuuPE9i1kAbevaZjZI0rRLt17pytlkCabddpoWkNn5pc9/3+ciL5MROcw88+bPbeXdoUwNToDmy7HVMctXzh7Un9HQWiWgpVrSf6C29d0VNGFS4XpKlr33jW6Yp6qNC3Qiej1AH7GzExEl8LW+n/R9Mh8NGMOaReNTAanTo9h09NHWjQiYaoS5hxvhqjmukZX0aZKUtqzN9tJqEAnoocAXA7gHCI6CmAtAAsAmPkeADcA+BgRjQGoAHg/t6DzdBYcEib4nbtSo0XodPyr6CgCWKckuefQxbC7n51qUAtkrxH9/f08NDSUyLWTJIpTVBCyTsHKT/JD+WsbqY4Jw68UqfAHI3QSRLSLmftV+8QT12ZMl59uyKIgZIluK6cNKtDVNqpUx3Hrpt3GuSUmTdTTlL3ZTkSgtxlTJ+7aFYtaPBJBiJ/R6gRKGlNKWG0j06gYE2E9VSNhRKC3GdMuRkOHjkdO+xeEtFAqV3Drpt3oW/9ETUA32mPXT5iwTluwRDsRgd5mvDG3QTz01BGsu3YRrNzkBH0rR2KOETJDuVKtad2mWnOY4FcpRe5bEiV3pBORaosJ4HruB4dL2kzQcWZtZA9QH58rCGnF1bpVoccqwgR/p0S8tQIR6AkysKSIVQ/vhirQKE8UGt61/pv7pF66kAmOlSt1JXNVEMz66KY9BT8pRKAngFdQT7dyyiSQpW/oDS1GdvK0WbaeICSNq3V7V6eqBhc3LZ3btsShTkxIEoHeZvwPcqU6YTsyCJhgWzNf+oZe7PzJiVrZUBc3vGvdln2ojk+gOt5YDkHOuZYgtAO/k9IVpF5hnifCjZedjzsGLp702cHhElY/sqfWNalUrmD1I3sANJc4lIV2co0giUVtRpdY5CZCmCRNNIuVI21bMUGIE1dBef4XFRwrV3B2wcKpM2NKZUSVYNS3/gllNnVPwcLutVc2PK6w9zDNSGJRigirGmmSNNEsIsyFdjHOjB3PHa918ipXqtqVpSpkUVcao1ypNtXkJug9bHUjnVYiAr3N6Dz4ZxcsKQsgTHmiZHiaJiKp0L2HPd1WXSvJRq+RBCLQ24wqhtbKEU6dGRNhLkx5/II2LOfCtOevH11fAmY03Fc4DYhTtM2oYmhHz4xJ+KEgAFi+cDaWbXiy9m5c/evnYtPTRwIDAFwzSVDEimr/ne+5uG7bKk1eSFZqw4hTNAVcsGar9PcUBGBSKVzA1pqvv6SI7ftHtCvY3m4LJ18Zm+QbsnKEje9drA2R1FV4zIKzVJyiKWeqFhISBD+qSozb949gx5orcPfKPqWZ5JXqeJ2jvzrB+MxjPwAQ3I/Yj2krybQiAj0FLF84GxR+mCBMSVxzh1sHyWtXP6tLnZgH2JUfB4dLkfoRN9NXOA2IDT1hbvq//44dzx2ftI1g94QcVTyovd0Wuqd1iQNVmDLM6SnUdShyCev4tXHbgcj9iLNcVkA09AS5fXBvnTAH7GXntK68cum3dsUi7fJTEDqNgpXH8oWza6GEQL1ZJohj5QqWL5yt3KfbnmVEoCfIQ0/pm0O/VKkGLv3cpWHBkj+h0Jm4z/z2/SMNJ9vN6Slg+/4R5T7d9iwjJpcE8ddq8TKnp6Bc+nnDr84uWC3t7i4ISeD3JzUaMug6M7MeihgFUe8SJE96V6jKq+6GX3nTqAWh02Dnx83SPDtC5y5VowudrbwTo8tEoCfIjZedr9y+7MJZSqdMO+q8CEKaqFTH8VIExYVhK0rexKKshyJGQUwuCeKWCn3oqSMYZ9aWEHXpxCWiIIQRNelunHlSKdyp1OFIMkVTjtdmniMKtLsD9Zl2gjBVSVN2Z5wEZYqKhp5i/CnLKmFu5Qgzp3ehPFqtaR66PqWCMJUolSuT6sKYaOVZ72IkAj3F6GzmeSJMMGsfuHVb9onDVJgShHXfcmPXTToSdUIXIxHoKcSbFadighkHN1yt/fy6axdNatslCGmn2FPAiVOnldnRQUR5xN36LboqjCqTprfmSxY0dxHoKcOkBV1YuNXAkiKGDh3HgzsPiz1dSDVu1cOhQ8fxwM7DLb+eN7DA369U559yNfUgzT0tphoJW0wZYaGJpuFW2/ePiDAXUg0BuPM9dkTXg00I895uqy4sUZfh4VWG1m3ZZ7SKzRMFVmv054ck2eVIBHrKCApNjFL5LSzEUUoGCEnjitKN2w40rHy49Y38ZTJuWjo3NPbcxM9UsPJazT2oD3BSXY5CTS5E9HUA1wB4kZnfpNhPAL4I4N0ARgH8LjM/E/dApwq6ynBRQ7B058kT4S/eZxf+lx6mQtK4ZooozJiWx+iZ8TrThl/R6Z83q2kzyHQrhxwBp87Ur5pdbT9Ked5WY6Km3QfgnQH73wXgIufnFgBfaX5YU5e4stp053GFuXuMlZNK7EJyuMI2CmfGJ3DXyj7sWHNFoIAeWFLEjjVX4OCGq5XHhvUrBYATo1WlMLfyVHsn01RaIFSgM/O3AdTXeH2V6wDczzY7AfQQ0blxDXCqoSuwD9jtsS5YsxXLNjwZap8zLtQv8lxIkJ5uK3KDl+o4x2LOWLtiEax8Yy/AjGldqSwtEEeUSxGAtw7sUWfbC/4DiegW2Fo85s6dG8OlOxN/lcVG42PDCvVv3HYgsPmuILSal0ar2PT0kcg29DjMGaqSAKYmSG99mTSVFohDoKumOOXfh5nvBXAvYKf+x3DtKUGQ06WZh0ZqwwhJMwFgogGlIkeEC9Zs1QpP0zBCv9LTt/4JI2ep35ySli5HcYQ6HAXgLRt4HoBjMZxXcGiV06UTy4cKU4NxZm2IYDNhhAEVrWukuVJjHAJ9C4APks1SAC8xc525RWicVjldpDm10AlUquP45MN7agK7mTDC8qheO89C02iTsMWHAFwO4BwiOgpgLQALAJj5HgCPww5Z/DHssMUPt2qwU5XVVy2oyx5tVku4fXCvZJIKHYO3ZG4zK9q4woaTIlSgM/ONIfsZwMdjG5FQR9xOl8HhkpEwz+cI455MOrc0b96gjK8gtBtXC9cJZZMVrU55Wr5wduTKjUkgtVwyQlSnS5BTyDQzb9yXFs2wNRW3T6OIdCFtHCtXcNfKvjqhTLBNjGGolKflC2dj865SJqowSv53B+IWHfI6hVY/8qqNsZns0GPlCgaWFPG2C2fFNFpBUENAXXx3GG5z9esvKU7yDzGAzbtKRo5Rf0LS9v0jqUntD0MEegeiKjpUnWCs27IPtw/ubercZxcsLPncE9jxXFCumSA0D8Mu3hXUTN2L16+kKk6nE8KDw6XApL00pfaHISaXlNNIWU5dHG25Um2qqp2VI5w6MybJSEJbcAX5RIC/xvXrFD3vxuBwSbsK9W83SdprxibfbkRDTzGtKMsZJIrD9KCZ07tEmAttw41cObugrrmSJ8JdK/vwvKdWi/vO6CBg0vuzbsu+UHNKmlL7wxCBnmIajac1KTqk4qalc7XL2zwRTgTE6AqCH1NTSRCV6jiI1Lb01xZsA4PXZLLq4d2B/QQYmFTHXLea9ZpTjOsipQAxuaSYRm13a1cswupH90TSprutHO4YuBj982YpOyaNM9eWt4JgQlyhrSdGq7h7ZV9dr9wTo1Xcumn35L6iBpf01jHXMaenkJouRFEQDT3FNJohOrCkiI03LEYxgo2v4vRydCMEVDDMizNKBqoQF3kiDCwpYsZZav0zauvcsDrmgB3imJYuRFEQgZ5imrHduaFXpoLVO0ls3z+iPc51QBH0dS+KPQXR5IXYGGfG4HAplqgSbzy6TjHq7bYyFaroRQR6ionDdmfiifdPEkEvTp6oFqN71/v66iYcK0cYPTNmPD4douELXoKco1HwxqPrFKa1KxZlKlTRi9jQU06zZTlVqcxWnjBjWhdeqlSVtsGgutBeu6g/q+7sgoVTZ8ZicZ6Khi94qVTHEVdzLVfTdmuzqOzkG7cdyEyoohcR6B1OI3VgglL7/XZ574SzbMOTRrWkBaERVK3ggMkKSk+3BebwBtCupq1TmFpREK8diEDPMI0W8Q9jYEkRQ4eO1xXwCnqgg5I5BKFV5Imw8YbFtRh006bTJoEFQDq6EEVBBHpGabQtnSluCKPJAx2WzCEIrWKCeVJCUVAMukuUwIK0C3A/ItAzSqva0nkxfaBVYxGEdtDjJNGZPoM5mhytkjWBHYZEuWSUNHnhG71mwcrjA0vnhoZBCoKOk6+MRQppdGPWsxJXHhUR6BmlVW3pGqGRa/YULFx/SRHb94/UTDo3XTY3crlUoXMhssNXg0oIVCe41tQiKlmIK4+KCPSMkqaCQaqx6F7BPJGdxn3tImzeVZqUibd5VwnXX1Ksaey93Ra6LXlEpywMHNxwdWgJgWPlivIZNCHtceVRERt6RkmTF96kywtgC/lxtjWqU6fHlD6A7ftHJvVuXLbhSYx22EsnmDGnp2BUv99tagG8+gy64YtursWp02PKUEaVZp/FGi4uItAzRlofNpUD1Y2SKZUrkwp7BYU3+jWmTtOgBDMIwPzXFfBASP1+76o0yImvioJRrWhbHT3WakSgZ4isPWzuC7Zsw5PGMeoMWyt3J6qgrFWhc2EAO39yIvCYPBHufM/FADCpgfPyhbMn+Wb8/XSDlKF2RI+1EuKEurf39/fz0NBQItfOKjrBWOwpTDJTpI0L1myNnMpfsPK1l/XWTbtjH5OQboohE7m74uvttnDylbG6lote3GcpSCC7K1/dNQm2PT8NENEuZu5X7ROPU4ZIU6hiFIKq2ulK/Hq1okYbdgjZZfVVCwKjW1zxfWK0GijMgfBoFm9nMB3eZzisB2mSiEDPEGkKVYxCUFW7oBK/pXIFF6zZCsCu4ihMHQaWFHHjZefHdr5SuVITvH6BrGpD58Vra29FW8g4ERt6hmhlwaBWOlvD7JdBdnKGrYVZeUJPwUK5UgUR4LcUFqx8La69VK4gTxSpY04OwEQjNye0BNcmflZXDqfH4vnL3PbYXgwdOj4p+irMP1NU2ODTbGMXG3rGaIXg1UUAtKtvomkdDq+vwOR7MD1vngivLXRJz9QpQJSJvqdgYcZZXZOeMV0VUlMbexzvb5ANXQS6EMnZ2ipN3nte3RMZ9tKoxgYg0Nnlnncq1F93VzFhoYB+oq52OgErZ6epevvyFqw8zurKKePZTQIT4lKcggS6mFwEY2fr7YN7J5XUjTNs0l9XPay5gF94+xOZ3LHd+Z6Lay9a3/on1HWyp4hEr1THIwtz1+RgWsmw3fQULBAhltVVT8GqJSKNKhq1VKrjmG7lULDyDZk922GuEaeoYORsHRwu1dVHB+Kth+E6q9xEJC9hjqkHdh4O7QG57tpFyvTwNCqfjoKYOO4KzA0hTRszzurC2hWLYvmuZpzVhYMbrsaONVegrJkgyqPVhttCtiNKTTR0wcjZunHbAa0SG8cD6V+OMl5VnE0cUzq8Y/N+Pu3JSlE72bcK9zsbWFJMLB/AytMk04eXY+WKtiGLlSeMjbPx4sv7rOgc9W6ZgUY06qBzxoVo6IJRM+ogoR3HA6kS0l5hvnHbgVqYWRRhnCOaFFI2sKSIHWuu0Ma/C6/i/456GmzSTGTnHIRVTtSx8YbF2r+X++zdMXAx7lrZN+kZ3njD4kjX8T7HrSh+146CekYaOhG9E8AXAeQBfJWZN/j2Xw7gHwAcdDY9xsyfi22UQsvxah2ufXrVpt0156JOuyAglgdSN2G4tnCvbTyKyXucWWnnT7uGnjQqQbPu2kVY/cie0EQe/3m8yoGbV2BK0VN4K2wVqdKcdasx/zOkOpf7+bgCANpRUC9UoBNRHsCXALwDwFEATxPRFmb+ke/Q7zDzNbGNTEgEXb2Y6y8pKqsn3rR0biwPpG7CyBMpNfcoQt3veBocLkX2g5qkmGeZfI7wmrO6ak5BlaBxf1//zX3GTsizuiYbAaLU5vEX3gKiC0OdOdFfi193v3GH7ba6rV1o2CIRvRXAOma+yvn9NgBg5js9x1wO4FNRBLqELaaToBBG1/TRCu1CF9IVZCsv9hRqpVLDhK035DGq2cYVAJuePqK15frHdeLUaYxWs5Wq5IbeqSKIvMJv+cLZxt+FS0/BwrprFwGo17R1Ywl6vqKEz6a1QmmjNBWHTkQ3AHgnM3/E+f1mAJcx8yc8x1wOYDNsDf4YbOG+T3GuWwDcAgBz58695NChQ43cj9BCdIW02lGcSPXi6ZbM/rjfsOJK3uNNi4UREDoOL17zgjZEMsUQgLtW9oUK3EajPL0F11Rllb3HhBXSSjIRLmmajUNXeTH8f89nAMxj5pNE9G4AgwAuqvsQ870A7gVsDd3g2kKbaYcnXoduOWpS7sD9rEnda5Nlv3/CWBUS4dHbbWHtikW18b+UMWEO2N+LSQRRoy9upTqOdVv21bIviyGlbnWkPf0+SUyiXI4C8FbJOQ+2Fl6DmV9m5pPO/x8HYBHRObGNUmgbaWpt52rdlep4LToiLO7XJGInrF2Z6n51E5rbUm/4s1dOuobu+LS01NPF+be6cme5Uq1rO7j6qgW1+O9m4rm9BbimKiYa+tMALiKiCwCUALwfwO94DyCi1wP4GTMzEV0Ke6L4RdyDFVpPkPOpnbZIv6btpp6fOj1mdA9B4wprV6a6L5VzjQDceNn5ymvpnHF/6pgcks681MX5tztGvxHNOmiFleaGL+3AqJaLY0a5G3bY4teZ+fNE9AcAwMz3ENEnAHwMwBiACoA/ZubvBZ1TnKLZot12yyDHZVL2Un/pg7CxBE2AqnMlgRtb7tr7Z0zL48zYRKiDOc5xR/XPhBVda7bhS9qdqFKcS2iadndLCnNcRrluXC9onN9B1EibdpLPEc7KkzJKp7fbwtW/fm5dCKuVI8ycblesVAn83m4LZ8YmcOqMXghH+bsMDpe0mavNOPCz4HCVjkVC07S7W1KYE9b0unE2JIjzO2hHl6mClYOVj56ZOT7BqGhqkHdP68IdAxfX+Sk2vncxhj97JZ7fcHVdxubdK/uwdsUiVDTCHKj/u4R1BRpYUgzNHm2EIIdrFpBaLoIR7Y5+CavwZ3rdOCMi4vwOznaadTRLjoDXTrfP5Za5LSr8HlFXA7qFuzsRBfkpVPv61j8R2kDEKzhNmqGb1CCKujrLaptHFxHoghGt7JakIigrMcp143xB4/oOBodLOHUm3LlrwgTbVQJ3r71Sud8VrnGZeNzJK6qgNJ28jpUrxpNwWPaoLuvZ+1nV/SUVthsHItAFI9pRh0J1zWaja3QvaI4IF6zZGni+weHSpAmlp2AZpYx7P68a98ZtByJlWYYRNjkNDpdiEebu5NWIoDRljpP9q0K1PWil0MjqrN2KS9yIQBeMaXUdilZcV2e6ccMgdcJocLiE1Y/umSR4y5UqNn3/CDa+d3HoeIKEXtzO0CDt0R2Hjt5uC+XRqlHUiusYXLbhSaWgXLdln/Z76e22Quu/uIJTZyKKqiU3sjpLQnGJExHoQkfjf0FzinZqKq1Np0VXJ9jI/q7TDtdt2Rdr2J9Ke/SuDFT36/3s2hVm9VV6u63aPesEYrlSxeBwSfndrF2xqG6CDCoIFoeW3Kj5JCnFJQ5EoAsdj/cF1ZVv9QupIC3OxP4eJPTi5PpLinUrC1VClgp/KF6Q8/TkK2M1YR2U2KOb7HSar3eb6xCNS0vOuvmkEUSgC1MKU60tSGiZLP0biWJRVZd04751vUC37x+Z9LtpNydvnXFg8qSnKizmrkwA4MSp09rzhpkzgiYfv/mrWS056+aTRpA4dCGThMUp6zCtVbP6qgXKGG4rR6Eani6KxcoRervVXX/cmjOq+G2/0PZSKlcm3b/J6iFMS9UVFnMFblBZ4Ch27nbEfLsdqqLUiskyItCFzNFMspBJ8S73uI03LJ4kgHsKlpFDVGd/nzndbmism1D8wgdA7T6DKJUruHXTbvStfwI9mgnDpdvKYbqVw6pNu7UTYZBQDtP+o5gzsh7znUbE5CJkjmaThUyX840u+7X289FqJDNAlGbYgG2ft3LBmaGV6kRNww5K2Fm1aXdkx21PwYr0fWU95juNiIYuZI60a3Y6geRuV2niKvNRI/dTnWAEVej1C2mViWNgSTGyMC9Y+VpHIlPSVKq5UxCBLmSOMIGZNFEEVZD5KKgGexBRO9+pJg5dnRTVlXu7rYaKV5mavwRzxOQiZI60h6M1a1ZxteagBsf+aofNoJo4mmmuHIUsx3ynERHoQubIQjiaqaAKMh8F3Wf/vFnKOjdR0U2EWfiOhXqkHrogJEizNda9WaGmb3KO7GqKIqSzSbNNogVBaBHNmo+8KwHTiooTDNy9sk8EeQciTlFBSJA4HYNhza+9ZKVhgxAN0dAFIUHi7F/ptXuXyhXkyNbGVaQlxFOIFxHoghAjUQR0K+qK+52xqrosQOtCPNPeYLnTEZOLIMRE1JIE7ahlsu5afamBuImzf6vQGCLQBSEmogrodmS8tjN5J+sNljsBMbkIQkxEFdCN1DJpxKTRruSdtJdkmAqIhi4IMRG1JEHUWiZpN2mkvSTDVEAEuiDERFQBHdUcknaThhTbSh4xuQhCTDSSLh/FHJJ2k4aUC0geEeiCECOttFdnoX64FNtKFjG5CEJGEJOGEIZo6IKQEdJo0pBEonQhAl0QMkSaTBrNZrrKZBA/RiYXInonER0goh8T0RrFfiKi/+3s/wERvTn+oQqCkCaaibpJewhmVgkV6ESUB/AlAO8C8EYANxLRG32HvQvARc7PLQC+EvM4BUFIGc1E3aQ9BDOrmGjolwL4MTP/hJnPAPgGgOt8x1wH4H622Qmgh4jOjXmsgiCkiGYSidIegplVTAR6EcARz+9HnW1RjwER3UJEQ0Q0NDIyEnWsgiCkiGaibiSrtDWYCHRVo29/lWWTY8DM9zJzPzP3z54922R8giCklGYKf0kIZmswiXI5CuB8z+/nATjWwDGCIHQYjUbdpDEEsxMwEehPA7iIiC4AUALwfgC/4ztmC4BPENE3AFwG4CVmfiHWkQqC0FGkKQSzUwgV6Mw8RkSfALANQB7A15l5HxH9gbP/HgCPA3g3gB8DGAXw4dYNWRAEQVBhlFjEzI/DFtrebfd4/s8APh7v0ARBEIQoSC0XQRCEDkEEuiAIQocgAl0QBKFDINv8ncCFiX4JICt5vucA+HnSgzBAxhk/WRlrVsYJZGesaR3nPGZWJvIkWW3xADP3J3h9Y4hoKAtjlXHGT1bGmpVxAtkZa1bG6UVMLoIgCB2CCHRBEIQOIUmBfm+C145KVsYq44yfrIw1K+MEsjPWrIyzRmJOUUEQBCFexOQiCILQIYhAFwRB6BDaLtCJ6OtE9CIR/bDd144CEZ1PRNuJ6Fki2kdEf5T0mHQQ0XQi+j4R7XHGuj7pMQVBRHkiGiaif0x6LDqI6Hki2ktEu4loKOnxBEFEPUT0KBHtd57XtyY9Jj9EtMD5Lt2fl4no1qTHpYKIVjnv0Q+J6CEimp70mExpuw2diH4TwEnYLeve1NaLR8BpoXcuMz9DRK8BsAvAADP/KOGh1UFEBGAGM58kIgvAdwH8kdMOMHUQ0R8D6AfwWma+JunxqCCi5wH0M3MaE0smQUR/A+A7zPxVIpoGoJuZywkPS4vTp7gE4DJmPpT0eLwQURH2+/NGZq4Q0cMAHmfm+5IdmRlt19CZ+dsAjrf7ulFh5heY+Rnn/78E8CwUbfXSgNPL9aTzq+X8pNLbTUTnAbgawFeTHksnQESvBfCbAL4GAMx8Js3C3OHtAJ5LmzD30AWgQERdALqRoWY9YkM3gIjmA1gC4KmEh6LFMWPsBvAigG8xc1rHejeATwOYSHgcYTCAJ4hoFxHdkvRgAngDgBEAf+2Ysb5KRDOSHlQI7wfwUNKDUMHMJQB/DuAwgBdgN+t5ItlRmSMCPQQimglgM4BbmfnlpMejg5nHmbkPdvu/S4kodeYsIroGwIvMvCvpsRiwjJnfDOBdAD7umArTSBeANwP4CjMvAXAKwJpkh6THMQldC+CRpMeigoh6AVwH4AIAcwDMIKIPJDsqc0SgB+DYozcDeJCZH0t6PCY4y+1/BfDOZEeiZBmAax379DcAXEFEDyQ7JDXMfMz590UAfw/g0mRHpOUogKOeFdmjsAV8WnkXgGeY+WdJD0TDbwE4yMwjzFwF8BiAtyU8JmNEoGtwHI1fA/AsM38h6fEEQUSziajH+X8B9kO5P9FBKWDm25j5PGaeD3vZ/SQzp077IaIZjiMcjvniSgCpjMpi5p8COEJEC5xNbweQOse9hxuRUnOLw2EAS4mo25EBb4ftP8sESYQtPgTg3wEsIKKjRPT77R6DIcsA3Axbi3RDrd6d9KA0nAtgOxH9AHZT728xc2pDAjPArwD4LhHtAfB9AFuZ+f8lPKYg/hDAg87fvw/AnyY7HDVE1A3gHbC13lTirHQeBfAMgL2wZWRmSgBI6r8gCEKHICYXQRCEDkEEuiAIQocgAl0QBKFDEIEuCILQIYhAFwRB6BBEoAuCIHQIItAFQRA6hP8PSDRrUXjDEh0AAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "data = pd.read_csv('data/task2_data.csv')\n",
    "data.head()\n",
    "\n",
    "fig1 = plt.subplot()\n",
    "plt.scatter(data.loc[:, '面积'], data.loc[:, '价格'])\n",
    "plt.title('Price VS Size')\n",
    "plt.show()\n",
    "\n",
    "fig2 = plt.subplot()\n",
    "plt.scatter(data.loc[:, '人均收入'], data.loc[:, '价格'])\n",
    "plt.title('Price VS Income')\n",
    "plt.show()\n",
    "\n",
    "fig3 = plt.subplot()\n",
    "plt.scatter(data.loc[:, '平均房龄'], data.loc[:, '价格'])\n",
    "plt.title('Price VS House Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 1) (5000, 1)\n"
     ]
    }
   ],
   "source": [
    "#x,y赋值\n",
    "x = data.loc[:, '面积']\n",
    "y = data.loc[:, '价格']\n",
    "\n",
    "# 数据格式转化\n",
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "# print(x.shape, y.shape)\n",
    "\n",
    "x = x.reshape(-1, 1)\n",
    "y = y.reshape(-1, 1)\n",
    "print(x.shape, y.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1273463.80967472]\n",
      " [1192500.07431933]\n",
      " [1420564.16140447]\n",
      " ...\n",
      " [1113233.11006996]\n",
      " [1237040.41730573]\n",
      " [1162515.70057855]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#线性回归(LinearRegression())\n",
    "model = LinearRegression()\n",
    "\n",
    "#训练模型\n",
    "model.fit(x, y)\n",
    "\n",
    "#结果预测\n",
    "y_predict = model.predict(x)\n",
    "print(y_predict)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig4 = plt.figure()\n",
    "plt.scatter(x, y, label='y_real')\n",
    "plt.plot(x, y_predict,'r', label='y_predict')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116859388203.27335 0.10766339222843158\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "#均方差(MSE)\n",
    "MSE = mean_squared_error(y, y_predict)\n",
    "# R2越接近1数据越准确\n",
    "R2 = r2_score(y, y_predict)\n",
    "\n",
    "print(MSE, R2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "           面积         人均收入      平均房龄\n0  188.581619  79245.63626  4.901877\n1  164.161571  78936.74809  4.688919\n2  232.949602  63236.99563  4.878289\n3  150.608655  65122.34212  3.577503\n4  153.862555  63628.64511  5.877775",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>面积</th>\n      <th>人均收入</th>\n      <th>平均房龄</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>188.581619</td>\n      <td>79245.63626</td>\n      <td>4.901877</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>164.161571</td>\n      <td>78936.74809</td>\n      <td>4.688919</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>232.949602</td>\n      <td>63236.99563</td>\n      <td>4.878289</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>150.608655</td>\n      <td>65122.34212</td>\n      <td>3.577503</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>153.862555</td>\n      <td>63628.64511</td>\n      <td>5.877775</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#x,y再次赋值\n",
    "x1 = data.drop(['价格'],axis=1)\n",
    "y = data.loc[:,'价格']\n",
    "x1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建立多因子回归模型,并训练\n",
    "model_multi = LinearRegression()\n",
    "model_multi.fit(x1,y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1463868.24688829 1445981.85185019 1253388.6205439  ... 1285670.68139457\n",
      " 1243839.71867445 1116875.92416746]\n"
     ]
    }
   ],
   "source": [
    "#多因子模型的预测\n",
    "y_predict_multi = model_multi.predict(x1)\n",
    "print(y_predict_multi)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58264450329.883 0.555093495178965\n"
     ]
    }
   ],
   "source": [
    "#均方差(MSE)\n",
    "MSE = mean_squared_error(y, y_predict_multi)\n",
    "# R2越接近1数据越准确\n",
    "R2 = r2_score(y, y_predict_multi)\n",
    "\n",
    "print(MSE, R2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化预测结果\n",
    "fig5 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(y,y_predict_multi)\n",
    "plt.xlabel('real price')\n",
    "plt.ylabel('prdicted price')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1235099.47156076]\n"
     ]
    }
   ],
   "source": [
    "# 面积160 人均收入70000,房龄5\n",
    "x_test = np.array([[160,70000,5]])\n",
    "y_test_predict = model_multi.predict(x_test)\n",
    "print(y_test_predict)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}